• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.458-463
• /
• 2023

This study presents a paper on the implementation of a Raspberry Pi-based educational smart farm system. It confirms that in a real smart farm environment, the control of temperature, humidity, soil moisture, and light intensity can be smoothly managed. It also includes remote monitoring and control of sensor information through a web service. Additionally, information about intruders collected by the Pi camera is transmitted to the administrator. Although the cost of existing smart farms varies depending on the location, material, and type of installation, it costs 400 million won for polytunnel and 1.5 billion won for glass greenhouses when constructing 0.5ha (1,500 pyeong) on average. Nevertheless, among the problems of smart farms, there are lax locks, malfunctions to automation, and errors in smart farm sensors (power problems, etc.). We believe that this study can protect crops at low cost if it is complementarily used to improve the security and reliability of expensive smart farms. The cost of using this study is about 100,000 won, so it can be used inexpensively even when applied to the area. In addition, in the case of plant cultivators, cultivators with remote control functions are sold for more than 1 million won, so they can be used as low-cost plant cultivators.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.07a
• /
• pp.249-250
• /
• 2017

본 논문은 Raspberry Pi를 이용하여 IoT 시대에 맞춘 화재 예방 시스템(Fire Prevention System)을 개발하였다. 오늘날 모바일 기기의 보급이 일상화 되어있어, 어디에서나 IoT 제품들을 제어할 수 있다. 이러한 IoT 기술을 접목하여, 화재가 발생하기 전, 가스 센서를 이용하여 가스누출을 미리 감지하여 스마트 폰에 메시지를 전달함과 동시에 가스밸브을 잠금으로서 화재를 미연에 방지하도록 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.65-67
• /
• 2018

We introduce a system that detects smoking in a specific area. The proposed system is implemented on a wireless ad hoc network consisting of Raspberry Pi boards. It is more economical owing to low-cost device than commercial smoking monitoring system and is scalable than the existing system with single Raspberry Pi. In this paper, the probability density function of carbon monoxide concentration during smoking and non-smoking is approximated as Gauusian distribution, respectively, using data measured from sensors for a long time. Based on this, a maximum likelihood detection technique is adopted to estimate the smoking status by observing the concentration of carbon monoxide. We aim at improving the reliability by estimating the smoking status using the collected values from multiple sensors connected to the ad hoc network.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.12
• /
• pp.2347-2356
• /
• 2017

Raspberry Pi has been widely used for a hardware platform to develop the Internet of Things (IoT) applications. The basic task of diverse IoT devices is to obtain their status or environmental information by using various sensors and to send them to a gateway or a remote server. For the purpose, one of application layer protocols for IoT, MQTT is widely used. IoT devices are often powered by batteries and they are required to operate for a long time without replacing or recharging their batteries. Thus, energy efficiency is one of the most critical problems for obtaining and sending sensor data. To develop energy-efficient IoT applications, the information about power consumption characteristics of the applications is necessary. However, there has been little study to analyze power consumption of IoT communication on IoT devices. This paper presents a study to measure and analyze the power consumption of sending/receiving data under various conditions via the MQTT protocol on Raspberry Pi.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.1
• /
• pp.165-172
• /
• 2017

Recently, many supports about the education linked with industrial field have been provided in technical specialized high schools. In order to keep pace with the global trend to emphasize software education and to move away from traditional grammar-based programming learning, various physical computing tools have been used in the education fields. For this study, we conducted a programming class using Raspberry Pi for technical high school students. In the class, students were instructed to produce actual results based on the knowledge they had learned. Project-based learning was used to help students create products and thus they performed tasks while discussing and collaborating on a team-by-team basis. In particular, self-regulation learning strategies were considered to provide effective project-based instruction. After the class, we interviewed the students' satisfaction with it. The results showed that the students' satisfaction was high, and the fellow teachers also had a lot of possibilities and expectations about the programming instruction and project-based learning using Raspberry Pi.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.1
• /
• pp.147-154
• /
• 2017

This paper aims to build a home security system based on IoT to monitor a home on a mobile phone. The system consists of data gathering sensors, camera, gateway and Xively platform. The Raspberry Pi collects data from the three sensors and sends the data to Xively, and sends the video stream of home to a client in a smart phone through a internet. The servers are composed of Xively, socket server in Raspberry Pi and E-mail server in Google. The proposed system transmits e-mail, text message, and video stream when there are motion, fire, and gas leakage, and can control the gas valve through Raspberry Pi. The experimental results show that a user gets 'emergency E-mail' and text message and watches the video stream of the home through WIFI or LTE on a smart phone.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.6
• /
• pp.375-381
• /
• 2019

Structural health monitoring to determine structural conditions at an early stage and to efficiently manage the energy requirements of buildings using systems that collects relevant data, is under active investigation. Structural monitoring requires cutting-edge technology in which construction, sensing, and ICT technologies are combined. However, the scope of application is limited because expensive sensors and specialized technical skills are often required. In this study, a Raspberry Pi module, one of the most widely used single board computers, a Lora module that is capable of long-distance communication at low power, and a high-performance accelerometer are used to construct a wireless edge computing system that can monitor building response over an extended time period. In addition, the Raspberry Pi module utilizes an edge computing algorithm, and only meaningful data is obtained from the vast amount of acceleration data acquired in real-time. The raw data acquired using Wi-Fi communication are compared to the Laura data to evaluate the accuracy of the data obtained using the system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.6
• /
• pp.565-570
• /
• 2015

In this paper, we implemented the smart door lock system that control remotely devices using the concept of internet of things. Internet of things is intelligent system that can help devices to communicate with people and devices. And recently internet of things is getting attention because of advance of hardware technology and big data. The smart doorlock system based on internet of things used raspberry pi, sensor and doorlock. Using the smart phone, doorlock can be controlled from the raspberry pi server. And the user can identify some people that is in front of doorlock. also user can check around of doorlock in realtime using the raspberry pi camera.

• Journal of Information Processing Systems
• /
• v.14 no.4
• /
• pp.989-1009
• /
• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.868-871
• /
• 2016

Recently, Unmanned vehicle and Wearable Technology using iot research is being carried out. The unmanned vehicle is the result of it technology. Robots, autonomous navigation vehicle and obstacle avoidance, data communications, power, and image processing, technology integration of a unmanned vehicle or an unmanned robot. The final goal of the unmanned vehicle manual not autonomous by destination safely and quickly reaching. This paper managed to cover One of the key skills of unmanned vehicle is to image processing. Currently battery technology of unmanned vehicle can drive for up to 1 hours. Therefore, we use the Raspberry Pi 3 to reduce power consumption to a minimum. Using the Raspberry Pi 3 and to develop an image recognition system. The goal is to propose a system that recognizes all the objects in the image received from the camera.